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Wang X, Pu J. Recent Advances in Cardiac Magnetic Resonance for Imaging of Acute Myocardial Infarction. SMALL METHODS 2024; 8:e2301170. [PMID: 37992241 DOI: 10.1002/smtd.202301170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/14/2023] [Indexed: 11/24/2023]
Abstract
Acute myocardial infarction (AMI) is one of the primary causes of death worldwide, with a high incidence and mortality rate. Assessment of the infarcted and surviving myocardium, along with microvascular obstruction, is crucial for risk stratification, treatment, and prognosis in patients with AMI. Nonionizing radiation, excellent soft tissue contrast resolution, a large field of view, and multiplane imaging make cardiac magnetic resonance (CMR) a "one-stop" method for assessing cardiac structure, function, perfusion, and metabolism. Hence, this imaging technology is considered the "gold standard" for evaluating myocardial function and viability in AMI. This review critically compares the advantages and disadvantages of CMR with other cardiac imaging technologies, and relates the imaging findings to the underlying pathophysiological processes in AMI. A more thorough understanding of CMR technology will clarify their advanced clinical diagnosis and prognostic assessment applications, and assess the future approaches and challenges of CMR in the setting of AMI.
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Affiliation(s)
- Xu Wang
- Shanghai Jiao Tong University, School of Medicine Affiliated Renji Hospital, Shanghai, 200127, China
| | - Jun Pu
- Shanghai Jiao Tong University, School of Medicine Affiliated Renji Hospital, Shanghai, 200127, China
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2
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Merton R, Bosshardt D, Strijkers GJ, Nederveen AJ, Schrauben EM, van Ooij P. Reproducibility of 3D thoracic aortic displacement from 3D cine balanced SSFP at 3 T without contrast enhancement. Magn Reson Med 2024; 91:466-480. [PMID: 37831612 DOI: 10.1002/mrm.29856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/02/2023] [Accepted: 08/16/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE Aortic motion has direct impact on the mechanical stresses acting on the aorta. In aortic disease, increased stiffness of the aorta may lead to decreased aortic motion over time, which could be a predictor for aortic dissection or rupture. This study investigates the reproducibility of obtaining 3D displacement and diameter maps quantified using accelerated 3D cine MRI at 3 T. METHODS A noncontrast-enhanced, free-breathing 3D cine sequence based on balanced SSFP and pseudo-spiral undersampling with high spatial isotropic resolution was developed (spatial/temporal resolution [1.6 mm]3 /67 ms). The thoracic aorta of 14 healthy volunteers was prospectively scanned three times at 3 T: twice on the same day and a third time 2 weeks later. Aortic displacement was calculated using iterative closest point nonrigid registration of manual segmentations of the 3D aorta at end-systole and mid-diastole. Interexamination and interobserver regional analysis of mean displacement for five regions of interest was performed using Bland-Altman analysis. Additionally, a complementary voxel-by-voxel analysis was done, allowing a more local inspection of the method. RESULTS No significant differences were found in mean and maximum displacement for any of the regions of interest for the interexamination and interobserver analysis. The maximum displacement measured in the lower half of the ascending aorta was 11.0 ± 3.4 mm (range: 3.0-17.5 mm) for the first scan. The smallest detectable change in mean displacement in the lower half of the ascending aorta was 3 mm. CONCLUSION Detailed 3D cine balanced SSFP at 3 T allows for reproducible quantification of systolic-diastolic mean aortic displacement within acceptable limits.
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Affiliation(s)
- Renske Merton
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Daan Bosshardt
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Gustav J Strijkers
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Biomedical Physics and Engineering, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Aart J Nederveen
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Eric M Schrauben
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Pim van Ooij
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
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Tunedal K, Viola F, Garcia BC, Bolger A, Nyström FH, Östgren CJ, Engvall J, Lundberg P, Dyverfeldt P, Carlhäll CJ, Cedersund G, Ebbers T. Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI-based personalized cardiovascular mathematical model. J Physiol 2023; 601:3765-3787. [PMID: 37485733 DOI: 10.1113/jp284652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/29/2023] [Indexed: 07/25/2023] Open
Abstract
Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. KEY POINTS: The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.
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Affiliation(s)
- Kajsa Tunedal
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Federica Viola
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Belén Casas Garcia
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ann Bolger
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Fredrik H Nyström
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl Johan Östgren
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Prevention, Rehabilitation and Community Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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Bissell MM, Raimondi F, Ait Ali L, Allen BD, Barker AJ, Bolger A, Burris N, Carhäll CJ, Collins JD, Ebbers T, Francois CJ, Frydrychowicz A, Garg P, Geiger J, Ha H, Hennemuth A, Hope MD, Hsiao A, Johnson K, Kozerke S, Ma LE, Markl M, Martins D, Messina M, Oechtering TH, van Ooij P, Rigsby C, Rodriguez-Palomares J, Roest AAW, Roldán-Alzate A, Schnell S, Sotelo J, Stuber M, Syed AB, Töger J, van der Geest R, Westenberg J, Zhong L, Zhong Y, Wieben O, Dyverfeldt P. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J Cardiovasc Magn Reson 2023; 25:40. [PMID: 37474977 PMCID: PMC10357639 DOI: 10.1186/s12968-023-00942-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023] Open
Abstract
Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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Affiliation(s)
- Malenka M Bissell
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), LIGHT Laboratories, Clarendon Way, University of Leeds, Leeds, LS2 9NL, UK.
| | | | - Lamia Ait Ali
- Institute of Clinical Physiology CNR, Massa, Italy
- Foundation CNR Tuscany Region G. Monasterio, Massa, Italy
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alex J Barker
- Department of Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Center, Aurora, USA
| | - Ann Bolger
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Nicholas Burris
- Department of Radiology, University of Michigan, Ann Arbor, USA
| | - Carl-Johan Carhäll
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Tino Ebbers
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Alex Frydrychowicz
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Julia Geiger
- Department of Diagnostic Imaging, University Children's Hospital, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea
| | - Anja Hennemuth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Albert Hsiao
- Department of Radiology, University of California, San Diego, CA, USA
| | - Kevin Johnson
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Liliana E Ma
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Duarte Martins
- Department of Pediatric Cardiology, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Marci Messina
- Department of Radiology, Northwestern Medicine, Chicago, IL, USA
| | - Thekla H Oechtering
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Department of Pediatric Cardiology, Division of Pediatrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cynthia Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Imaging, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jose Rodriguez-Palomares
- Department of Cardiology, Hospital Universitari Vall d´Hebron,Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red-CV, CIBER CV, Madrid, Spain
| | - Arno A W Roest
- Department of Pediatric Cardiology, Willem-Alexander's Children Hospital, Leiden University Medical Center and Center for Congenital Heart Defects Amsterdam-Leiden, Leiden, The Netherlands
| | | | - Susanne Schnell
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Physics, Institute of Physics, University of Greifswald, Greifswald, Germany
| | - Julio Sotelo
- School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile
| | - Matthias Stuber
- Département de Radiologie Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Ali B Syed
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Johannes Töger
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos Westenberg
- CardioVascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liang Zhong
- National Heart Centre Singapore, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yumin Zhong
- Department of Radiology, School of Medicine, Shanghai Children's Medical Center Affiliated With Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Oliver Wieben
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Petter Dyverfeldt
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Hurd ER, Han M, Mendes JK, Hadley JR, Johnson CR, DiBella EVR, Oshinski JN, Timmins LH. Comparison of Prospective and Retrospective Gated 4D Flow Cardiac MR Image Acquisitions in the Carotid Bifurcation. Cardiovasc Eng Technol 2023; 14:1-12. [PMID: 35618870 DOI: 10.1007/s13239-022-00630-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 05/06/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE To evaluate the agreement of 4D flow cMRI-derived bulk flow features and fluid (blood) velocities in the carotid bifurcation using prospective and retrospective gating techniques. METHODS Prospective and retrospective ECG-gated three-dimensional (3D) cine phase-contrast cardiac MRI with three-direction velocity encoding (i.e., 4D flow cMRI) data were acquired in ten carotid bifurcations from men (n = 3) and women (n = 2) that were cardiovascular disease-free. MRI sequence parameters were held constant across all scans except temporal resolution values differed. Velocity data were extracted from the fluid domain and evaluated across the entire volume or at defined anatomic planes (common, internal, external carotid arteries). Qualitative agreement between gating techniques was performed by visualizing flow streamlines and topographical images, and statistical comparisons between gating techniques were performed across the fluid volume and defined anatomic regions. RESULTS Agreement in the kinematic data (e.g., bulk flow features and velocity data) were observed in the prospectively and retrospectively gated acquisitions. Voxel differences in time-averaged, peak systolic, and diastolic-averaged velocity magnitudes between gating techniques across all volunteers were 2.7%, 1.2%, and 6.4%, respectively. No significant differences in velocity magnitudes or components ([Formula: see text], [Formula: see text], [Formula: see text]) were observed. Importantly, retrospective acquisitions captured increased retrograde flow in the internal carotid artery (i.e., carotid sinus) compared to prospective acquisitions (10.4 ± 6.3% vs. 4.6 ± 5.3%; [Formula: see text] < 0.05). CONCLUSION Prospective and retrospective ECG-gated 4D flow cMRI acquisitions provide comparable evaluations of fluid velocities, including velocity vector components, in the carotid bifurcation. However, the increased temporal coverage of retrospective acquisitions depicts increased retrograde flow patterns (i.e., disturbed flow) not captured by the prospective gating technique.
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Affiliation(s)
- Elliott R Hurd
- Department of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Rm. 3100, Salt Lake City, UT, 84112, USA
| | - Mengjiao Han
- School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA
| | - Jason K Mendes
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, USA
| | - J Rock Hadley
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, USA
| | - Chris R Johnson
- Department of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Rm. 3100, Salt Lake City, UT, 84112, USA
- School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA
| | - Edward V R DiBella
- Department of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Rm. 3100, Salt Lake City, UT, 84112, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84112, USA
| | - John N Oshinski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Lucas H Timmins
- Department of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Rm. 3100, Salt Lake City, UT, 84112, USA.
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA.
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Park J, Kim J, Lee J. Multivariable Technique for the Evaluation of the Trans-stenotic Pressure Gradient. Cardiovasc Eng Technol 2023; 14:104-114. [PMID: 35879586 DOI: 10.1007/s13239-022-00638-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 06/09/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE This study establishes a reliable image-based multivariable technique for measuring the trans-stenotic pressure gradient. METHODS A self-made in vitro steady flow model based on adjustable velocities and stenotic properties were used as the experimental subject. The pre-stenotic flow velocity, severity, and length of the stenosis were used as the input variables. Based on equations used to fit the plots of the physically measured pressure gradient values versus each input variable, a multivariable formula for the pressure gradient measurement could then be derived. The flow model was scanned using velocity-encoded phase-contrast magnetic resonance imaging (PC-MRI) to validate the derived formula while simultaneously measuring the trans-stenotic pressure gradient. The correlation between the physically-measured pressure gradient values and the pressure gradient values calculated using the new formula were subsequently analyzed. RESULTS The results of linear regression analysis using the physically measured pressure gradient values for the new method were compared to values obtained using the simplified Bernoulli equation (R2, 0.991, and 0.975, respectively). In a paired t-test, no statistically significant difference was found between the new method and the physical measurements. CONCLUSIONS The derived multivariable technique was found to reliably measure the trans-stenotic pressure gradient, with better performance than a traditional procedure based on the simplified Bernoulli equation.
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Affiliation(s)
- Jieun Park
- Nonlinear Dynamics Research Center, Kyungpook National University, Daegu, Republic of Korea
| | - Junghun Kim
- Bio-Medical Research Institute, Kyungpook National University & Hospital, Daegu, Korea
| | - Jongmin Lee
- Department of Radiology, Kyungpook National University & Hospital, 50, Samduk 2-ga, Jung Gu, Daegu, 700-721, Republic of Korea.
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Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, Tee YK, Dhanalakshmi S, Lai KW. An Overview of Deep Learning Methods for Left Ventricle Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4208231. [PMID: 36756163 PMCID: PMC9902166 DOI: 10.1155/2023/4208231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/25/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023]
Abstract
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Raza Ali
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azira Khalil
- Faculty of Science & Technology, Universiti Sains Islam Malaysia, Nilai 71800, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
| | - Khin Wee Lai
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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8
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Bustamante M, Viola F, Engvall J, Carlhäll C, Ebbers T. Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning. J Magn Reson Imaging 2023; 57:191-203. [PMID: 35506525 PMCID: PMC10946960 DOI: 10.1002/jmri.28221] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue. PURPOSE To develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI. STUDY TYPE Retrospective. SUBJECTS A total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets. FIELD STRENGTH/SEQUENCE A 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI). ASSESSMENT A 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations. STATISTICAL TESTS Dice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters. RESULTS The following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively. DATA CONCLUSION The addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mariana Bustamante
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
| | - Federica Viola
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Jan Engvall
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Carl‐Johan Carlhäll
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Tino Ebbers
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
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9
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Peper ES, van Ooij P, Jung B, Huber A, Gräni C, Bastiaansen JAM. Advances in machine learning applications for cardiovascular 4D flow MRI. Front Cardiovasc Med 2022; 9:1052068. [PMID: 36568555 PMCID: PMC9780299 DOI: 10.3389/fcvm.2022.1052068] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.
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Affiliation(s)
- Eva S. Peper
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland,*Correspondence: Eva S. Peper,
| | - Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands,Department of Pediatric Cardiology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bernd Jung
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Adrian Huber
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jessica A. M. Bastiaansen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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10
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Sundin J, Bustamante M, Ebbers T, Dyverfeldt P, Carlhäll CJ. Turbulent Intensity of Blood Flow in the Healthy Aorta Increases With Dobutamine Stress and is Related to Cardiac Output. Front Physiol 2022; 13:869701. [PMID: 35694404 PMCID: PMC9174892 DOI: 10.3389/fphys.2022.869701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/22/2022] [Indexed: 11/22/2022] Open
Abstract
Introduction: The blood flow in the normal cardiovascular system is predominately laminar but operates close to the threshold to turbulence. Morphological distortions such as vascular and valvular stenosis can cause transition into turbulent blood flow, which in turn may cause damage to tissues in the cardiovascular system. A growing number of studies have used magnetic resonance imaging (MRI) to estimate the extent and degree of turbulent flow in different cardiovascular diseases. However, the way in which heart rate and inotropy affect turbulent flow has not been investigated. In this study we hypothesized that dobutamine stress would result in higher turbulence intensity in the healthy thoracic aorta. Method: 4D flow MRI data were acquired in twelve healthy subjects at rest and with dobutamine, which was infused until the heart rate increased by 60% when compared to rest. A semi-automatic segmentation method was used to segment the thoracic aorta in the 4D flow MR images. Subsequently, flow velocity and several turbulent kinetic energy (TKE) parameters were calculated in the ascending aorta, aortic arch, descending aorta and whole thoracic aorta. Results: With dobutamine infusion there was an increase in heart rate (66 ± 9 vs. 108 ± 13 bpm, p < 0.001) and stroke volume (88 ± 13 vs. 102 ± 25 ml, p < 0.01). Additionally, there was an increase in Peak Average velocity (0.7 ± 0.1 vs. 1.2 ± 0.2 m/s, p < 0.001, Peak Max velocity (1.3 ± 0.1 vs. 2.0 ± 0.2 m/s, p < 0.001), Peak Total TKE (2.9 ± 0.7 vs. 8.0 ± 2.2 mJ, p < 0.001), Peak Median TKE (36 ± 7 vs. 93 ± 24 J/m3, p = 0.002) and Peak Max TKE (176 ± 33 vs. 334 ± 69 J/m3, p < 0.001). The relation between cardiac output and Peak Total TKE in the whole thoracic aorta was very strong (R2 = 0.90, p < 0.001). Conclusion: TKE of blood flow in the healthy thoracic aorta increases with dobutamine stress and is strongly related to cardiac output. Quantification of such turbulence intensity parameters with cardiac stress may serve as a risk assessment of aortic disease development.
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Affiliation(s)
- Jonathan Sundin
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Mariana Bustamante
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping, Sweden
| | - Tino Ebbers
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping, Sweden
| | - Petter Dyverfeldt
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping, Sweden
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- *Correspondence: Carl-Johan Carlhäll,
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11
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Corrado PA, Seiter DP, Wieben O. Automatic measurement plane placement for 4D Flow MRI of the great vessels using deep learning. Int J Comput Assist Radiol Surg 2022; 17:199-210. [PMID: 34403045 PMCID: PMC8851604 DOI: 10.1007/s11548-021-02475-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/03/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE Despite the great potential and flexibility of 4D flow MRI for hemodynamic analysis, a major limitation is the need for time-consuming and user-dependent post-processing. We propose a fast four-step algorithm for rapid, robust, and repeatable flow measurements in the great vessels based on automatic placement of measurement planes and vessel segmentation. METHODS Our algorithm works by (1) subsampling the 3D image into 3D patches, (2) predicting the probability of each patch containing individual vessels and location/orientation of the vessel within the patch via a convolutional neural network, (3) selecting the predicted planes with highest probabilities for each vessel, and (4) shifting the plane centers to the maximum velocity within each plane. The method was trained on 283 scans and evaluated on 40 unseen scans by comparing algorithm-derived processing times, plane locations, and flow measurements to those of two manual observers (graduate students) using t-tests, Pearson correlation, and Bland-Altman analysis. RESULTS The average processing time for the algorithm (18 s) was shorter than observer 1 (362 s; P < 0.001) and observer 2 (317 s; P < 0.001). The distance between planes placed by the algorithm and those placed by manual observers was slightly greater (O1 vs. algorithm: 9.0 mm, O2 vs. algorithm: 10.3 mm) than the distance between planes placed by the two manual observers (8.3 mm). The correlation between flow values for planes placed by the algorithm and those placed by manual observers was slightly lower (O1 vs. algorithm: R = 0.68, O2 vs. algorithm: R = 0.72) than the flow correlation between the two manual observers (R = 0.81). CONCLUSION Our method is a feasible and accurate approach for fast, reproducible, and automated flow measurement and visualization in 4D flow MRI of the great vessels, with similar variability compared to a manual annotator as the variability between two manual observers. This approach could be applied in other anatomical regions.
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Affiliation(s)
- Philip A. Corrado
- Department of Medical Physics, University of Wisconsin-Madison,
Madison, Wisconsin, USA
| | - Daniel P. Seiter
- Department of Medical Physics, University of Wisconsin-Madison,
Madison, Wisconsin, USA
| | - Oliver Wieben
- Departments of Medical Physics and Radiology, University of
Wisconsin-Madison, Madison, Wisconsin, USA
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12
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Trenti C, Dyverfeldt P. Editorial for "Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi-Site, Multi-Vendor, and Multi-Label Dense U-Net". J Magn Reson Imaging 2021; 55:1681-1682. [PMID: 34816520 DOI: 10.1002/jmri.28005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 11/12/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Chiara Trenti
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Unit of Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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13
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Fujiwara T, Berhane H, Scott MB, Englund EK, Schäfer M, Fonseca B, Berthusen A, Robinson JD, Rigsby CK, Browne LP, Markl M, Barker AJ. Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi-Site, Multi-Vendor, and Multi-Label Dense U-Net. J Magn Reson Imaging 2021; 55:1666-1680. [PMID: 34792835 DOI: 10.1002/jmri.27995] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Automated segmentation using convolutional neural networks (CNNs) have been developed using four-dimensional (4D) flow magnetic resonance imaging (MRI). To broaden usability for congenital heart disease (CHD), training with multi-institution data is necessary. However, the performance impact of heterogeneous multi-site and multi-vendor data on CNNs is unclear. PURPOSE To investigate multi-site CNN segmentation of 4D flow MRI for pediatric blood flow measurement. STUDY TYPE Retrospective. POPULATION A total of 174 subjects across two sites (female: 46%; N = 38 healthy controls, N = 136 CHD patients). Participants from site 1 (N = 100), site 2 (N = 74), and both sites (N = 174) were divided into subgroups to conduct 10-fold cross validation (10% for testing, 90% for training). FIELD STRENGTH/SEQUENCE 3 T/1.5 T; retrospectively gated gradient recalled echo-based 4D flow MRI. ASSESSMENT Accuracy of the 3D CNN segmentations trained on data from single site (single-site CNNs) and data across both sites (multi-site CNN) were evaluated by geometrical similarity (Dice score, human segmentation as ground truth) and net flow quantification at the ascending aorta (Qs), main pulmonary artery (Qp), and their balance (Qp/Qs), between human observers, single-site and multi-site CNNs. STATISTICAL TESTS Kruskal-Wallis test, Wilcoxon rank-sum test, and Bland-Altman analysis. A P-value <0.05 was considered statistically significant. RESULTS No difference existed between single-site and multi-site CNNs for geometrical similarity in the aorta by Dice score (site 1: 0.916 vs. 0.915, P = 0.55; site 2: 0.906 vs. 0.904, P = 0.69) and for the pulmonary arteries (site 1: 0.894 vs. 0.895, P = 0.64; site 2: 0.870 vs. 0.869, P = 0.96). Qs site-1 medians were 51.0-51.3 mL/cycle (P = 0.81) and site-2 medians were 66.7-69.4 mL/cycle (P = 0.84). Qp site-1 medians were 46.8-48.0 mL/cycle (P = 0.97) and site-2 medians were 76.0-77.4 mL/cycle (P = 0.98). Qp/Qs site-1 medians were 0.87-0.88 (P = 0.97) and site-2 medians were 1.01-1.03 (P = 0.43). Bland-Altman analysis for flow quantification found equivalent performance. DATA CONCLUSION Multi-site CNN-based segmentation and blood flow measurement are feasible for pediatric 4D flow MRI and maintain performance of single-site CNNs. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Takashi Fujiwara
- Department of Radiology, Section of Pediatric Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Haben Berhane
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois, USA
| | - Michael B Scott
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois, USA.,Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Erin K Englund
- Department of Radiology, Section of Pediatric Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michal Schäfer
- Division of Cardiology, Heart Institute, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Brian Fonseca
- Department of Pediatrics, Section of Pediatric Cardiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Alexander Berthusen
- Department of Radiology, Section of Pediatric Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Joshua D Robinson
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Division of Pediatric Cardiology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.,Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Cynthia K Rigsby
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Lorna P Browne
- Department of Radiology, Section of Pediatric Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michael Markl
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois, USA.,Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Alex J Barker
- Department of Radiology, Section of Pediatric Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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14
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Ngo MT, Lee UY, Ha H, Jung J, Lee DH, Kwak HS. Improving Blood Flow Visualization of Recirculation Regions at Carotid Bulb in 4D Flow MRI Using Semi-Automatic Segmentation with ITK-SNAP. Diagnostics (Basel) 2021; 11:diagnostics11101890. [PMID: 34679588 PMCID: PMC8534781 DOI: 10.3390/diagnostics11101890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/06/2021] [Accepted: 10/10/2021] [Indexed: 11/16/2022] Open
Abstract
Assessment of carotid bulb hemodynamics using four-dimensional (4D) flow magnetic resonance imaging (MRI) requires accurate segmentation of recirculation regions that is frequently hampered by limited resolution. This study aims to improve the accuracy of 4D flow MRI carotid bulb segmentation and subsequent recirculation regions analysis. Time-of-flight (TOF) MRI and 4D flow MRI were performed on bilateral carotid artery bifurcations in seven healthy volunteers. TOF-MRI data was segmented into 3D geometry for computational fluid dynamics (CFD) simulations. ITK-SNAP segmentation software was included in the workflow for the semi-automatic generation of 4D flow MRI angiographic data. This study compared the velocities calculated at the carotid bifurcations and the 3D blood flow visualization at the carotid bulbs obtained by 4D flow MRI and CFD. By applying ITK-SNAP segmentation software, an obvious improvement in the 4D flow MRI visualization of the recirculation regions was observed. The 4D flow MRI images of the recirculation flow characteristics of the carotid artery bulbs coincided with the CFD. A reasonable agreement was found in terms of velocity calculated at the carotid bifurcation between CFD and 4D flow MRI. However, the dispersion of velocity data points relative to the local errors of measurement in 4D flow MRI remains. Our proposed strategy showed the feasibility of improving recirculation regions segmentation and the potential for reliable blood flow visualization in 4D flow MRI. However, quantitative analysis of recirculation regions in 4D flow MRI with ITK-SNAP should be enhanced for use in clinical situations.
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Affiliation(s)
- Minh Tri Ngo
- Department of Radiology of Hue Central Hospital, Hue, Thua Thien Hue 530000, Vietnam;
| | - Ui Yun Lee
- Division of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeon-ju 54896, Korea; (U.Y.L.); (J.J.)
| | - Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, Korea;
| | - Jinmu Jung
- Division of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeon-ju 54896, Korea; (U.Y.L.); (J.J.)
- Hemorheology Research Institute, Jeonbuk National University, Jeon-ju 54896, Korea
| | - Dong Hwan Lee
- Division of Mechanical Design Engineering, College of Engineering, Jeonbuk National University, Jeon-ju 54896, Korea; (U.Y.L.); (J.J.)
- Hemorheology Research Institute, Jeonbuk National University, Jeon-ju 54896, Korea
- Correspondence: (D.H.L.); (H.S.K.); Tel.: +82-63-270-3998 (D.H.L.); +82-63-250-2582 (H.S.K.)
| | - Hyo Sung Kwak
- Department of Radiology and Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeon-ju 54907, Korea
- Correspondence: (D.H.L.); (H.S.K.); Tel.: +82-63-270-3998 (D.H.L.); +82-63-250-2582 (H.S.K.)
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15
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van Ooij P, Farag ES, Blanken CPS, Nederveen AJ, Groenink M, Planken RN, Boekholdt SM. Fully quantitative mapping of abnormal aortic velocity and wall shear stress direction in patients with bicuspid aortic valves and repaired coarctation using 4D flow cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2021; 23:9. [PMID: 33588887 PMCID: PMC7885343 DOI: 10.1186/s12968-020-00703-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 12/20/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Helices and vortices in thoracic aortic blood flow measured with 4D flow cardiovascular magnetic resonance (CMR) have been associated with aortic dilation and aneurysms. Current approaches are semi-quantitative or when fully quantitative based on 2D plane placement. In this study, we present a fully quantitative and three-dimensional approach to map and quantify abnormal velocity and wall shear stress (WSS) at peak systole in patients with a bicuspid aortic valve (BAV) of which 52% had a repaired coarctation. METHODS 4D flow CMR was performed in 48 patients with BAV and in 25 healthy subjects at a spatiotemporal resolution of 2.5 × 2.5 × 2.5mm3/ ~ 42 ms and TE/TR/FA of 2.1 ms/3.4 ms/8° with k-t Principal Component Analysis factor R = 8. A 3D average of velocity and WSS direction was created for the normal subjects. Comparing BAV patient data with the 3D average map and selecting voxels deviating between 60° and 120° and > 120° yielded 3D maps and volume (in cm3) and surface (in cm2) quantification of abnormally directed velocity and WSS, respectively. Linear regression with Bonferroni corrected significance of P < 0.0125 was used to compare abnormally directed velocity volume and WSS surface in the ascending aorta with qualitative helicity and vorticity scores, with local normalized helicity (LNH) and quantitative vorticity and with patient characteristics. RESULTS The velocity volumes > 120° correlated moderately with the vorticity scores (R ~ 0.50, P < 0.001 for both observers). For WSS surface these results were similar. The velocity volumes between 60° and 120° correlated moderately with LNH (R = 0.66) but the velocity volumes > 120° did not correlate with quantitative vorticity. For abnormal velocity and WSS deviating between 60° and 120°, moderate correlations were found with aortic diameters (R = 0.50-0.70). For abnormal velocity and WSS deviating > 120°, additional moderate correlations were found with age and with peak velocity (stenosis severity) and a weak correlation with gender. Ensemble maps showed that more than 60% of the patients had abnormally directed velocity and WSS. Additionally, abnormally directed velocity and WSS was higher in the proximal descending aorta in the patients with repaired coarctation than in the patients where coarctation was never present. CONCLUSION The possibility to reveal directional abnormalities of velocity and WSS in 3D provides a new tool for hemodynamic characterization in BAV disease.
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Affiliation(s)
- Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Emile S. Farag
- Department of Cardiothoracic Surgery, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands
| | - Carmen P. S. Blanken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Aart J. Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Maarten Groenink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Cardiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands
| | - R. Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - S. Matthijs Boekholdt
- Department of Cardiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands
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16
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Kroeger JR, Pavesio FC, Mörsdorf R, Weiss K, Bunck AC, Baeßler B, Maintz D, Giese D. Velocity quantification in 44 healthy volunteers using accelerated multi-VENC 4D flow CMR. Eur J Radiol 2021; 137:109570. [PMID: 33596498 DOI: 10.1016/j.ejrad.2021.109570] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 01/25/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND To evaluate the feasibility of a k-t accelerated multi-VENC 4D phase contrast flow MRI acquisition of the main heart-surrounding vessels, its benefits over a traditional single-VENC acquisition and to present reference flow and velocity values in a large cohort of volunteers. METHODS 44 healthy volunteers were examined on a 3 T MRI scanner (Ingenia, Philips, Best, The Netherlands). 4D flow measurements were obtained with a FOV including the aorta and the pulmonary arteries. VENC values were set to 40, 100 and 200 cm/s and unfolded based on an MRI signal model. Unfolded multi-VENC data was compared to the single-VENC with VENC 200 cm/s. Flow and velocity quantification was performed in several regions of interest (ROI) placed in the ascending aorta and in the main pulmonary artery. Conservation of mass analysis was performed for single- and multi-VENC datasets. Values for mean and maximal flow velocity and stroke volume were calculated and compared to the literature. RESULTS Mean scan time was 13.8 ± 4 min. Differences between stroke volumes between the ascending aorta and the main pulmonary artery were significantly lower in multi-VENC datasets compared to single-VENC datasets (9.6 ± 7.8 mL vs. 25.4 ± 26.4 mL, p < 0.001). This was also true for differences in stroke volume between up- and downstream ROIs in the ascending aorta and pulmonary artery. Values for mean and maximal velocities and stroke volume were in-line with previous studies. To highlight potential clinical applications two exemplary 4D flow measurements in patients with different pathologies are shown and compared to single-VENC datasets. CONCLUSIONS k-t accelerated multi-VENC 4D phase contrast flow MRI acquisition of the great vessels is feasible in a clinically acceptable scan duration. It offers improvements over traditional single-VENC 4D flow, expectedly being valuable when vessels with different flow velocities or complex flow phenomena are evaluated.
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Affiliation(s)
- Jan Robert Kroeger
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Germany.
| | - Francesca Claudia Pavesio
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
| | - Richard Mörsdorf
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
| | - Kilian Weiss
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Philips GmbH, Hamburg, Germany.
| | - Alexander Christian Bunck
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
| | - Bettina Baeßler
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
| | - David Maintz
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
| | - Daniel Giese
- Department of Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
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17
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Non-invasive estimation of relative pressure for intracardiac flows using virtual work-energy. Med Image Anal 2020; 68:101948. [PMID: 33383332 DOI: 10.1016/j.media.2020.101948] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 12/10/2020] [Accepted: 12/11/2020] [Indexed: 01/18/2023]
Abstract
Intracardiac blood flow is driven by differences in relative pressure, and assessing these is critical in understanding cardiac disease. Non-invasive image-based methods exist to assess relative pressure, however, the complex flow and dynamically moving fluid domain of the intracardiac space limits assessment. Recently, we proposed a method, νWERP, utilizing an auxiliary virtual field to probe relative pressure through complex, and previously inaccessible flow domains. Here we present an extension of νWERP for intracardiac flow assessments, solving the virtual field over sub-domains to effectively handle the dynamically shifting flow domain. The extended νWERP is validated in an in-silico benchmark problem, as well as in a patient-specific simulation model of the left heart, proving accurate over ranges of realistic image resolutions and noise levels, as well as superior to alternative approaches. Lastly, the extended νWERP is applied on clinically acquired 4D Flow MRI data, exhibiting realistic ventricular relative pressure patterns, as well as indicating signs of diastolic dysfunction in an exemplifying patient case. Summarized, the extended νWERP approach represents a directly applicable implementation for intracardiac flow assessments.
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18
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Juffermans JF, Westenberg JJM, van den Boogaard PJ, Roest AAW, van Assen HC, van der Palen RLF, Lamb HJ. Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers. J Magn Reson Imaging 2020; 53:1268-1279. [PMID: 33179389 PMCID: PMC7984392 DOI: 10.1002/jmri.27431] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 12/21/2022] Open
Abstract
Background Hemodynamic aorta parameters can be derived from 4D flow MRI, but this requires lumen segmentation. In both commercially available and research 4D flow MRI software tools, lumen segmentation is mostly (semi‐)automatically performed and subsequently manually improved by an observer. Since the segmentation variability, together with 4D flow MRI data and image processing algorithms, will contribute to the reproducibility of patient‐specific flow properties, the observer's lumen segmentation reproducibility and repeatability needs to be assessed. Purpose To determine the interexamination, interobserver reproducibility, and intraobserver repeatability of aortic lumen segmentation on 4D flow MRI. Study Type Prospective and retrospective. Population A healthy volunteer cohort of 10 subjects who underwent 4D flow MRI twice. Also, a clinical cohort of six subjects who underwent 4D flow MRI once. Field Strength/Sequence 3T; time‐resolved three‐directional and 3D velocity‐encoded sequence (4D flow MRI). Assessment The thoracic aorta was segmented on the 4D flow MRI in five systolic phases. By positioning six planes perpendicular to a segmentation's centerline, the aorta was divided into five segments. The volume, surface area, centerline length, maximal diameter, and curvature radius were determined for each segment. Statistical Tests To assess the reproducibility, the coefficient of variation (COV), Pearson correlation coefficient (r), and intraclass correlation coefficient (ICC) were calculated. Results The interexamination and interobserver reproducibility and intraobserver repeatability were comparable for each parameter. For both cohorts there was very good reproducibility and repeatability for volume, surface area, and centerline length (COV = 10–32%, r = 0.54–0.95 and ICC = 0.65–0.99), excellent reproducibility and repeatability for maximal diameter (COV = 3–11%, r = 0.94–0.99, ICC = 0.94–0.99), and good reproducibility and repeatability for curvature radius (COV = 25–62%, r = 0.73–0.95, ICC = 0.84–0.97). Data Conclusion This study demonstrated no major reproducibility and repeatability limitations for 4D flow MRI aortic lumen segmentation. Level of Evidence 3 Technical Efficacy Stage 2
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Affiliation(s)
- Joe F Juffermans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos J M Westenberg
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Arno A W Roest
- Department of Paediatric Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hans C van Assen
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Roel L F van der Palen
- Department of Paediatric Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hildo J Lamb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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19
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Sakly H, Said M, Tagina M. Evaluation of the active contour and topographic watershed segmentation "assessment of the systolic ejection fraction in the left ventricular for medical assistance in 5D short axis cine MRI". Heliyon 2020; 6:e05547. [PMID: 33294690 PMCID: PMC7689516 DOI: 10.1016/j.heliyon.2020.e05547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 10/03/2020] [Accepted: 11/16/2020] [Indexed: 11/30/2022] Open
Abstract
A comparative study has been depicted between the contour and topographic watershed segmentation approach for short-axis 5D cardiac sequences with MRI for medical decision. The fifth dimension has been defined as the excitation of pixels based on the gray scale around the myocardium without consideration of the morphological structure of the heart in 3D and fourth dimension (time). Three patients were performed the first is healthy, the second has a genetic disease, and the third had a heart failure syndrome for a dimension ROI = 150mm, average age is 54 years old, and mean of weight = 86 kg. A contouring and watershed segmentation algorithm for a sample of 63 Cine Fiesta MRI sequences for short-axis cuts with Matlab and its in-box toolbox complements was implemented. For a healthy patient 13.4% tolerance rate for the estimation of the stroke fraction, 6.4% for a patient with genetic disease, 8.7% error rate for a patient with heart failure symptom. The results show that the regurgitation fraction by the contour approach for a patient case with symptom of the presence of a genetic disease is 0.0335% for an aortic valve, 0.248% for a mitral valve, an error rate 0.16% for estimating this parameter for the aortic orifice with the watershed segmentation approach. In return, for a patient with suspected heart failure (stenosis or regurgitation) the regurgitation fraction is estimated by 0% for aortic valve, 1.49 e + 03% for a mitral valve, an error rate 11.76% compared to the watershed segmentation approach. The results are validated clinically. The Optimization of the topographic watershed approach with mutual information was simulated for the extraction of measurements (ejection fraction, regurgitation rate) within the left ventricle for three patient types (healthy, genetic pathology and heart failure). The results are considered interesting compared to the clinical assessment.
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Affiliation(s)
- Houneida Sakly
- COSMOS Laboratory, National School of Computer Sciences (ENSI), University of Manouba, Tunisia
| | - Mourad Said
- Radiology and Medical Imaging Unit, International Center Carthage Medical, Tourist Area "JINEN EL OUEST" 5000 Monastir, Tunisia
| | - Moncef Tagina
- COSMOS Laboratory, National School of Computer Sciences (ENSI), University of Manouba, Tunisia
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Dottori J, Casciaro M, Craiem D, El-Batti S, Mousseaux E, Alsac JM, Larrabide I. Regional assessment of vascular morphology and hemodynamics: methodology and evaluation for abdominal aortic aneurysms after endovascular repair. Comput Methods Biomech Biomed Engin 2020; 23:1060-1070. [DOI: 10.1080/10255842.2020.1786073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Javier Dottori
- Pladema - CONICET, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina
| | - Mariano Casciaro
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro - CONICET, Buenos Aires, Argentina
| | - Damian Craiem
- Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro - CONICET, Buenos Aires, Argentina
| | | | | | | | - Ignacio Larrabide
- Pladema - CONICET, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina
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Abstract
Magnetic resonance imaging (MRI) has become an important tool for the clinical evaluation of patients with cardiac and vascular diseases. Since its introduction in the late 1980s, quantitative flow imaging with MRI has become a routine part of standard-of-care cardiothoracic and vascular MRI for the assessment of pathological changes in blood flow in patients with cardiovascular disease. More recently, time-resolved flow imaging with velocity encoding along all three flow directions and three-dimensional (3D) anatomic coverage (4D flow MRI) has been developed and applied to enable comprehensive 3D visualization and quantification of hemodynamics throughout the human circulatory system. This article provides an overview of the use of 4D flow applications in different cardiac and vascular regions in the human circulatory system, with a focus on using 4D flow MRI in cardiothoracic and cerebrovascular diseases.
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Affiliation(s)
- Gilles Soulat
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Patrick McCarthy
- Division of Cardiac Surgery, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Michael Markl
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois 60208, USA
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22
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Four-dimensional Flow Magnetic Resonance Imaging Quantification of Blood Flow in Bicuspid Aortic Valve. J Thorac Imaging 2020; 35:383-388. [PMID: 32453278 DOI: 10.1097/rti.0000000000000535] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Four-dimensional (D) flow magnetic resonance imaging (MRI) is limited by time-consuming and nonstandardized data analysis. We aimed to test the efficiency and interobserver reproducibility of a dedicated 4D flow MRI analysis workflow. MATERIALS AND METHODS Thirty retrospectively identified patients with bicuspid aortic valve (BAV, age=47.8±11.8 y, 9 male) and 30 healthy controls (age=48.8±12.5 y, 21 male) underwent Aortic 4D flow MRI using 1.5 and 3 T MRI systems. Two independent readers performed 4D flow analysis on a dedicated workstation including preprocessing, aorta segmentation, and placement of four 2D planes throughout the aorta for quantification of net flow, peak velocity, and regurgitant fraction. 3D flow visualization using streamlines was used to grade aortic valve outflow jets and extent of helical flow. RESULTS 4D flow analysis workflow time for both observers: 5.0±1.4 minutes per case (range=3 to 10 min). Valve outflow jets and flow derangement was visible in all 30 BAV patients (both observers). Net flow, peak velocity, and regurgitant fraction was significantly elevated in BAV patients compared with controls except for regurgitant fraction in plane 4 (91.1±29.7 vs. 62.6±19.6 mL/s, 37.1% difference; 121.7±49.7 vs. 90.9±26.4 cm/s, 28.9% difference; 9.3±10.1% vs. 2.0±3.4%, 128.0% difference, respectively; P<0.001). Excellent intraclass correlation coefficient agreement for net flow: 0.979, peak velocity: 0.931, and regurgitant fraction: 0.928. CONCLUSION Our study demonstrates the potential of an efficient data analysis workflow to perform standardized 4D flow MRI processing in under 10 minutes and with good-to-excellent reproducibility for flow and velocity quantification in the thoracic aorta.
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23
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Gottwald LM, Peper ES, Zhang Q, Coolen BF, Strijkers GJ, Nederveen AJ, van Ooij P. Pseudo-spiral sampling and compressed sensing reconstruction provides flexibility of temporal resolution in accelerated aortic 4D flow MRI: A comparison with k-t principal component analysis. NMR IN BIOMEDICINE 2020; 33:e4255. [PMID: 31957927 PMCID: PMC7079056 DOI: 10.1002/nbm.4255] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Time-resolved three-dimensional phase contrast MRI (4D flow) of aortic blood flow requires acceleration to reduce scan time. Two established techniques for highly accelerated 4D flow MRI are k-t principal component analysis (k-t PCA) and compressed sensing (CS), which employ either regular or random k-space undersampling. The goal of this study was to gain insights into the quantitative differences between k-t PCA- and CS-derived aortic blood flow, especially for high temporal resolution CS 4D flow MRI. METHODS The scan protocol consisted of both k-t PCA and CS accelerated 4D flow MRI, as well as a 2D flow reference scan through the ascending aorta acquired in 15 subjects. 4D flow scans were accelerated with factor R = 8. For CS accelerated scans, we used a pseudo-spiral Cartesian sampling scheme, which could additionally be reconstructed at higher temporal resolution, resulting in R = 13. 4D flow data were compared with the 2D flow scan in terms of flow, peak flow and stroke volume. A 3D peak systolic voxel-wise velocity and wall shear stress (WSS) comparison between k-t PCA and CS 4D flow was also performed. RESULTS The mean difference in flow/peak flow/stroke volume between the 2D flow scan and the 4D flow CS with R = 8 and R = 13 was 4.2%/9.1%/3.0% and 5.3%/7.1%/1.9%, respectively, whereas for k-t PCA with R = 8 the difference was 9.7%/25.8%/10.4%. In the voxel-by-voxel 4D flow comparison we found 13.6% and 3.5% lower velocity and WSS values of k-t PCA compared with CS with R = 8, and 15.9% and 5.5% lower velocity and WSS values of k-t PCA compared with CS with R = 13. CONCLUSION Pseudo-spiral accelerated 4D flow acquisitions in combination with CS reconstruction provides a flexible choice of temporal resolution. We showed that our proposed strategy achieves better agreement in flow values with 2D reference scans compared with using k-t PCA accelerated acquisitions.
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Affiliation(s)
- Lukas M. Gottwald
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Eva S. Peper
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Qinwei Zhang
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Bram F. Coolen
- Department of Biomedical Engineering and Physics, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Gustav J. Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Aart J. Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
| | - Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of Amsterdamthe Netherlands
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24
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Berhane H, Scott M, Elbaz M, Jarvis K, McCarthy P, Carr J, Malaisrie C, Avery R, Barker AJ, Robinson JD, Rigsby CK, Markl M. Fully automated 3D aortic segmentation of 4D flow MRI for hemodynamic analysis using deep learning. Magn Reson Med 2020; 84:2204-2218. [PMID: 32167203 DOI: 10.1002/mrm.28257] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/18/2020] [Accepted: 02/24/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions. METHODS A total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland-Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40. RESULTS Convolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930-0.966), Hausdorff distance of 2.80 (2.13-4.35), and average symmetrical surface distance of 0.176 (0.119-0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network-based analysis (median Dice score: 0.953-0.959; Hausdorff distance: 2.24-2.91; and average symmetrical surface distance: 0.145-1.98 to observers) demonstrated similar reproducibility. CONCLUSIONS Deep learning enabled fast and automated 3D aortic segmentation from 4D-flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.
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Affiliation(s)
- Haben Berhane
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Michael Scott
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
| | - Mohammed Elbaz
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
| | - Kelly Jarvis
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Patrick McCarthy
- Divison of Cardiac Surgery, Northwestern University, Chicago, Illinois
| | - James Carr
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois
| | - Chris Malaisrie
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Ryan Avery
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Alex J Barker
- Anschutz Medical Campus, University of Colorado, Aurora, Colorado
| | - Joshua D Robinson
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Cynthia K Rigsby
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Michael Markl
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois.,Department of Radiology, Northwestern University, Chicago, Illinois
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25
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Siriapisith T, Kusakunniran W, Haddawy P. Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces. J Digit Imaging 2019; 31:490-504. [PMID: 29352385 DOI: 10.1007/s10278-018-0049-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.
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Affiliation(s)
- Thanongchai Siriapisith
- Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.,Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, 73170, Thailand.
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, 73170, Thailand
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27
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EVCMR: A tool for the quantitative evaluation and visualization of cardiac MRI data. Comput Biol Med 2019; 111:103334. [DOI: 10.1016/j.compbiomed.2019.103334] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 05/24/2019] [Accepted: 06/17/2019] [Indexed: 01/18/2023]
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28
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Bidhult S, Hedström E, Carlsson M, Töger J, Steding-Ehrenborg K, Arheden H, Aletras AH, Heiberg E. A new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images. Clin Physiol Funct Imaging 2019; 39:327-338. [PMID: 31102479 PMCID: PMC6852024 DOI: 10.1111/cpf.12582] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 05/10/2019] [Indexed: 11/29/2022]
Abstract
Blood flow measurements in the ascending aorta and pulmonary artery from phase-contrast magnetic resonance images require accurate time-resolved vessel segmentation over the cardiac cycle. Current semi-automatic segmentation methods often involve time-consuming manual correction, relying on user experience for accurate results. The purpose of this study was to develop a semi-automatic vessel segmentation algorithm with shape constraints based on manual vessel delineations for robust segmentation of the ascending aorta and pulmonary artery, to evaluate the proposed method in healthy volunteers and patients with heart failure and congenital heart disease, to validate the method in a pulsatile flow phantom experiment, and to make the method freely available for research purposes. Algorithm shape constraints were extracted from manual reference delineations of the ascending aorta (n = 20) and pulmonary artery (n = 20) and were included in a semi-automatic segmentation method only requiring manual delineation in one image. Bias and variability (bias ± SD) for flow volume of the proposed algorithm versus manual reference delineations were 0·0 ± 1·9 ml in the ascending aorta (n = 151; seven healthy volunteers; 144 heart failure patients) and -1·7 ± 2·9 ml in the pulmonary artery (n = 40; 25 healthy volunteers; 15 patients with atrial septal defect). Interobserver bias and variability were lower (P = 0·008) for the proposed semi-automatic method (-0·1 ± 0·9 ml) compared to manual reference delineations (1·5 ± 5·1 ml). Phantom validation showed good agreement between the proposed method and timer-and-beaker flow volumes (0·4 ± 2·7 ml). In conclusion, the proposed semi-automatic vessel segmentation algorithm can be used for efficient analysis of flow and shunt volumes in the aorta and pulmonary artery.
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Affiliation(s)
- Sebastian Bidhult
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden.,Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Erik Hedström
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden.,Department of Clinical Sciences Lund, Diagnostic Radiology, Skane University Hospital, Lund University, Lund, Sweden
| | - Marcus Carlsson
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden
| | - Johannes Töger
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden
| | - Katarina Steding-Ehrenborg
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden.,Department of Health Sciences, Physiotherapy, Lund University, Lund, Sweden
| | - Håkan Arheden
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden
| | - Anthony H Aletras
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden.,Laboratory of Computing, Medical Informatics and Biomedical - Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Einar Heiberg
- Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund University, Lund, Sweden.,Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
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van Ooij P, Cibis M, Rowland EM, Vernooij MW, van der Lugt A, Weinberg PD, Wentzel JJ, Nederveen AJ. Spatial correlations between MRI-derived wall shear stress and vessel wall thickness in the carotid bifurcation. Eur Radiol Exp 2018; 2:27. [PMID: 30302598 PMCID: PMC6177500 DOI: 10.1186/s41747-018-0058-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 06/29/2018] [Indexed: 11/17/2022] Open
Abstract
Background To explore the possibility of creating three-dimensional (3D) estimation models for patient-specific wall thickness (WT) maps using patient-specific and cohort-averaged WT, wall shear stress (WSS), and vessel diameter maps in asymptomatic atherosclerotic carotid bifurcations. Methods Twenty subjects (aged 75 ± 6 years [mean ± standard deviation], eight women) underwent a 1.5-T MRI examination. Non-gated 3D phase-contrast gradient-echo images and proton density-weighted echo-planar images were retrospectively assessed for WSS, diameter estimation, and WT measurements. Spearman’s ρ and scatter plots were used to determine correlations between individual WT, WSS, and diameter maps. A bootstrapping technique was used to determine correlations between 3D cohort-averaged WT, WSS, and diameter maps. Linear regression between the cohort-averaged WT, WSS, and diameter maps was used to predict individual 3D WT. Results Spearman’s ρ averaged over the subjects was − 0.24 ± 0.18 (p < 0.001) and 0.07 ± 0.28 (p = 0.413) for WT versus WSS and for WT versus diameter relations, respectively. Cohort-averaged ρ, averaged over 1000 bootstraps, was − 0.56 (95% confidence interval [− 0.74,− 0.38]) for WT versus WSS and 0.23 (95% confidence interval [− 0.06, 0.52]) for WT versus diameter. Scatter plots did not reveal relationships between individual WT and WSS or between WT and diameter data. Linear relationships between these parameters became apparent after averaging over the cohort. Spearman’s ρ between the original and predicted WT maps was 0.21 ± 0.22 (p < 0.001). Conclusions With a combination of bootstrapping and cohort-averaging methods, 3D WT maps can be predicted from the individual 3D WSS and diameter maps. The methodology may help to elucidate pathological processes involving WSS in carotid atherosclerosis. Electronic supplementary material The online version of this article (10.1186/s41747-018-0058-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | - Merih Cibis
- Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands
| | - Ethan M Rowland
- Departments of Bioengineering, Imperial College London, London, UK
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Peter D Weinberg
- Departments of Bioengineering, Imperial College London, London, UK
| | - Jolanda J Wentzel
- Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands
| | - Aart J Nederveen
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Bustamante M, Gupta V, Forsberg D, Carlhäll CJ, Engvall J, Ebbers T. Automated multi-atlas segmentation of cardiac 4D flow MRI. Med Image Anal 2018; 49:128-140. [DOI: 10.1016/j.media.2018.08.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 08/07/2018] [Accepted: 08/10/2018] [Indexed: 11/16/2022]
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Khalil A, Ng SC, Liew YM, Lai KW. An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment. Cardiol Res Pract 2018; 2018:1437125. [PMID: 30159169 PMCID: PMC6109558 DOI: 10.1155/2018/1437125] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/05/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
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Affiliation(s)
- Azira Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Englund R, Palmerius KL, Hotz I, Ynnerman A. Touching Data: Enhancing Visual Exploration of Flow Data with Haptics. Comput Sci Eng 2018. [DOI: 10.1109/mcse.2018.03221931] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Manning WJ. Review of Journal of Cardiovascular Magnetic Resonance (JCMR) 2015-2016 and transition of the JCMR office to Boston. J Cardiovasc Magn Reson 2017; 19:108. [PMID: 29284487 PMCID: PMC5747150 DOI: 10.1186/s12968-017-0423-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 12/07/2017] [Indexed: 02/06/2023] Open
Abstract
The Journal of Cardiovascular Magnetic Resonance (JCMR) is the official publication of the Society for Cardiovascular Magnetic Resonance (SCMR). In 2016, the JCMR published 93 manuscripts, including 80 research papers, 6 reviews, 5 technical notes, 1 protocol, and 1 case report. The number of manuscripts published was similar to 2015 though with a 12% increase in manuscript submissions to an all-time high of 369. This reflects a decrease in the overall acceptance rate to <25% (excluding solicited reviews). The quality of submissions to JCMR continues to be high. The 2016 JCMR Impact Factor (which is published in June 2016 by Thomson Reuters) was steady at 5.601 (vs. 5.71 for 2015; as published in June 2016), which is the second highest impact factor ever recorded for JCMR. The 2016 impact factor means that the JCMR papers that were published in 2014 and 2015 were on-average cited 5.71 times in 2016.In accordance with Open-Access publishing of Biomed Central, the JCMR articles are published on-line in the order that they are accepted with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful to annually summarize the publications into broad areas of interest or themes, so that readers can view areas of interest in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes with previously published JCMR papers to guide continuity of thought in the journal. In addition, I have elected to open this publication with information for the readership regarding the transition of the JCMR editorial office to the Beth Israel Deaconess Medical Center, Boston and the editorial process.Though there is an author publication charge (APC) associated with open-access to cover the publisher's expenses, this format provides a much wider distribution/availability of the author's work and greater manuscript citation. For SCMR members, there is a substantial discount in the APC. I hope that you will continue to send your high quality manuscripts to JCMR for consideration. Importantly, I also ask that you consider referencing recent JCMR publications in your submissions to the JCMR and elsewhere as these contribute to our impact factor. I also thank our dedicated Associate Editors, Guest Editors, and reviewers for their many efforts to ensure that the review process occurs in a timely and responsible manner and that the JCMR continues to be recognized as the leading publication in our field.
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Affiliation(s)
- Warren J Manning
- From the Journal of Cardiovascular Magnetic Resonance Editorial Office and the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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Phellan R, Lindner T, Helle M, Falcao AX, Forkert ND. Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images. IEEE Trans Biomed Eng 2017; 65:1486-1494. [PMID: 28991731 DOI: 10.1109/tbme.2017.2759730] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a separate segmentation of each image. This paper introduces a method that combines both approaches to overcome the specific drawbacks of each technique. METHODS Vessels in the tAIP are enhanced by using the ranking orientation responses of path operators and multiscale vesselness enhancement filters. Then, tAIP segmentation is performed using a seed-based algorithm. In parallel, this algorithm is also used to segment each frame of the series and identify small vessels, which might have been lost in the tAIP segmentation. The results of each individual time frame segmentation are fused using an or boolean operation. Finally, small vessels found only in the fused segmentation are added to the tAIP segmentation. RESULTS In a quantitative analysis using ten 4D ASL MRA image series from healthy volunteers, the proposed combined approach reached an average Dice coefficient of 0.931, being more accurate than the corresponding tMIP, tAIP, and single time frame segmentation methods with statistical significance. CONCLUSION The novel combined vessel segmentation strategy can be used to obtain improved vessel segmentation results from 4D ASL MRA and other dynamic series of images. SIGNIFICANCE Improved vessel segmentation of 4D ASL MRA allows a fast and accurate assessment of cerebrovascular structures.
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Bustamante M, Gupta V, Carlhäll CJ, Ebbers T. Improving visualization of 4D flow cardiovascular magnetic resonance with four-dimensional angiographic data: generation of a 4D phase-contrast magnetic resonance CardioAngiography (4D PC-MRCA). J Cardiovasc Magn Reson 2017; 19:47. [PMID: 28645326 PMCID: PMC5481950 DOI: 10.1186/s12968-017-0360-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 05/09/2017] [Indexed: 11/10/2022] Open
Abstract
Magnetic Resonance Angiography (MRA) and Phase-Contrast MRA (PC-MRA) approaches used for assessment of cardiovascular morphology typically result in data containing information from the entire cardiac cycle combined into one 2D or 3D image. Information specific to each timeframe of the cardiac cycle is, however, lost in this process. This study proposes a novel technique, called Phase-Contrast Magnetic Resonance CardioAngiography (4D PC-MRCA), that utilizes the full potential of 4D Flow CMR when generating temporally resolved PC-MRA data to improve visualization of the heart and major vessels throughout the cardiac cycle. Using non-rigid registration between the timeframes of the 4D Flow CMR acquisition, the technique concentrates information from the entire cardiac cycle into an angiographic dataset at one specific timeframe, taking movement over the cardiac cycle into account. Registration between the timeframes is used once more to generate a time-resolved angiography. The method was evaluated in ten healthy volunteers. Visual comparison of the 4D PC-MRCAs versus PC-MRAs generated from 4D Flow CMR using the traditional approach was performed by two observers using Maximum Intensity Projections (MIPs). The 4D PC-MRCAs resulted in better visibility of the main anatomical regions of the cardiovascular system, especially where cardiac or vessel motion was present. The proposed method represents an improvement over previous PC-MRA generation techniques that rely on 4D Flow CMR, as it effectively utilizes all the information available in the acquisition. The 4D PC-MRCA can be used to visualize the motion of the heart and major vessels throughout the entire cardiac cycle.
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Affiliation(s)
- Mariana Bustamante
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Vikas Gupta
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Physiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Pennell DJ, Baksi AJ, Prasad SK, Mohiaddin RH, Alpendurada F, Babu-Narayan SV, Schneider JE, Firmin DN. Review of Journal of Cardiovascular Magnetic Resonance 2015. J Cardiovasc Magn Reson 2016; 18:86. [PMID: 27846914 PMCID: PMC5111217 DOI: 10.1186/s12968-016-0305-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 11/02/2016] [Indexed: 12/14/2022] Open
Abstract
There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication.
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Affiliation(s)
- D. J. Pennell
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - A. J. Baksi
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. K. Prasad
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - R. H. Mohiaddin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - F. Alpendurada
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. V. Babu-Narayan
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - J. E. Schneider
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - D. N. Firmin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
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Ha H, Kim GB, Kweon J, Lee SJ, Kim YH, Lee DH, Yang DH, Kim N. Hemodynamic Measurement Using Four-Dimensional Phase-Contrast MRI: Quantification of Hemodynamic Parameters and Clinical Applications. Korean J Radiol 2016; 17:445-62. [PMID: 27390537 PMCID: PMC4936168 DOI: 10.3348/kjr.2016.17.4.445] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 04/22/2016] [Indexed: 11/21/2022] Open
Abstract
Recent improvements have been made to the use of time-resolved, three-dimensional phase-contrast (PC) magnetic resonance imaging (MRI), which is also named four-dimensional (4D) PC-MRI or 4D flow MRI, in the investigation of spatial and temporal variations in hemodynamic features in cardiovascular blood flow. The present article reviews the principle and analytical procedures of 4D PC-MRI. Various fluid dynamic biomarkers for possible clinical usage are also described, including wall shear stress, turbulent kinetic energy, and relative pressure. Lastly, this article provides an overview of the clinical applications of 4D PC-MRI in various cardiovascular regions.
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Affiliation(s)
- Hojin Ha
- POSTECH Biotech Center, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Guk Bae Kim
- Asan Institute of Life Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Jihoon Kweon
- Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Sang Joon Lee
- POSTECH Biotech Center, Pohang University of Science and Technology, Pohang 37673, Korea.; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Young-Hak Kim
- Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Deok Hee Lee
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Dong Hyun Yang
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Namkug Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.; Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
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Turski P, Scarano A, Hartman E, Clark Z, Schubert T, Rivera L, Wu Y, Wieben O, Johnson K. Neurovascular 4DFlow MRI (Phase Contrast MRA): emerging clinical applications. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40809-016-0019-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Sdika M. Enhancing atlas based segmentation with multiclass linear classifiers. Med Phys 2015; 42:7169-81. [DOI: 10.1118/1.4935946] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
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